As the number of cancer patients with stable disease increases, the long-term cardiotoxic effects of treatment with anti-cancer drugs become increasingly apparent. Cardiovascular-related mortality is 7-fold higher in childhood cancer survivors than expected from an age matched normal population and ~12% of breast cancer survivors suffer heart failure within three years of treatment. Tyrosine kinase inhibitors (TKIs) are exemplars of promising anti-cancer drugs whose use is complicated by cardiotoxicity. Whether this reflects drug-mediated inhibition of the same signaling pathways as the anti-oncogenic effects of these drugs is unclear. Cardiotoxic phenotypes induced by chemically diverse TKIs include life-threatening heart failure and cardiac infarction, and are likely to include targets that are not the nominal ones; this is particularly true of poly- selective TKIs. Identifying the precise mechanisms of TKI-mediated cardiotoxicity has the potential to substantially improve cancer care and also reveal fundamental aspects of cardiac cell biology. My project exploits the recent development of reproducible means to transdifferentiate human induced pluripotent stem cell into cardiomyocytes (hiPSC-CMs) and probe the biology of these cells using omic and ?systems biology? methods. I have collected extensive preliminary data on hiPSC-CMs responses to over 50 different drug perturbations and am in the midst of using statistical regression and machine learning algorithms to define and study the molecular networks associated with TKI-induced cardiotoxicity. I have already found that cardiac energy metabolism is dysregulated by many cardiotoxic drugs and could be biomarker of cardiotoxicity more broadly. In this proposal, I will build on my profiling data through focused study of Sorafenib, a drug that I have shown to increase anaerobic glycolysis in hiPSC-CMs. As a multi-target drug, Sorafenib inhibits multiple kinases and gene networks. Identifying targets relevant to cardiotoxicity is a key goal of my research. I will tackle this by establishing the sequential onset of molecular networks after drug treatment and their causal relation with the increased glycolysis phenotype. Single cell heterogeneous molecular responses will be analyzed to find non-genetic mechanisms that determine the differential sensitivity of cardiotoxicity to Sorafenib. I will build quantitative models to fit and explain data at population and single cell levels. Ultimately, to find means to increase therapeutic effects, I want to systematically compare target expression, phenotypic responses and molecular mechanisms induced by Sorafenib in both hiPSC-CMs and hepatocellular carcinoma cells (treated by Sorafenib clinically). My study will generate pilot methods in converting ?big data? into mechanistic insights in cardiac biology, and facilitate development of new therapies to mitigate cardiotoxicity without compromising efficacy of cancer elimination.